Volatility, returns, and price trends highlight risk and growth signals.
Visual Encodings of Market Behavior: A Multi-View Exploration of Returns and Risks
Nuoyan Yang 1
nuoyan.yang@uwaterloo.ca
1 Faculty of Mathematics, University of Waterloo
Introduction
Investors and analysts often face challenges when interpreting stock performance due to market volatility and rapid shifts. To address this, we present a comprehensive analysis of four influential tech companies, which are Apple (AAPL), Tesla (TSLA), Meta (META), and Microsoft (MSFT) starting from 2024. Using normalized price trends, volatility metrics, monthly returns, and compelling visualizations, this poster simplifies complex patterns and uncovers key performance signals. Together, these metrics provide a clearer view of relative growth, stability, and risk across leading tech stocks. This multi-view approach supports more informed decision-making in fast-moving financial environments.
📈 (Option 3 - Base-100 Index)
Market Overview
We begin by normalizing stock prices to facilitate interactive meaningful comparisons.
Figure 1: Normalized adjusted prices indexed to 100 at the starting date, allowing intuitive comparison of relative growth across tech stocks. Reference Link
The base-100 index method sets each stock’s starting price to 100, simplifying comparisons across stocks regardless of their initial price differences and providing foundational insights. By doing so, we can immediately identify distinct trajectories: Meta’s early growth versus Tesla’s fluctuating performance. Notably, Microsoft’s and Apple’s steady upward trends contrast with Tesla’s sharp ups and downs, revealing differences in stability among these stocks. This chart also captures the surge in volatility that emerged as a direct response to the “trade war.” Such normalized comparisons are fundamental for quickly assessing relative performance and identifying notable turning points.
🔄 (Option 1 - 30-Day Rolling)
Volatility and Returns
We then move beyond simple price tracking to dig into the risks and returns underlying these trends.
Figure 2: Hexbin plot showing 30-day rolling return vs. volatility, colored by median adjusted close price. Brighter regions reflect higher price levels tied to specific risk–return combinations.
To further interpret stock price behavior, we analyze 30-day rolling returns and volatility. Rolling returns, computed as the average daily return over a 30-day window, capture short-term performance trends. Volatility, measured as the standard deviation of daily returns, reflects how much a stock’s price fluctuates, indicating its risk level. By plotting return against volatility and coloring each region by the median adjusted closing price, we highlight how price levels vary across different short-term risk-return profiles. For instance, higher-priced zones under low volatility (e.g., MSFT) may signal stable growth, while price dispersion across a wider risk band (e.g., TSLA) reflects more uncertain, risk-driven valuations.
🧠 (Option 6 - Manim Visualization)
Summary of Recent Trends
To summarize recent price dynamics vividly, we continue with an engaging visualization.
We use a creatively animated visualization with fancy elements to capture recent stock price movements over the past three months. Unlike traditional charts, this stylized representation makes complex information visually appealing and memorable. This animation visually tracks short-term price trajectories, using motion and color to highlight daily ups and downs. This dynamic view draws attention to emerging patterns and reinforces recent momentum shifts with clarity and impact.
💡 Did You Know?
In April 2025, global stock markets faced their biggest drop since the COVID-19 pandemic. Within two days, the S&P 500 lost over 10% of its value after U.S. tariffs were imposed and rising trade tensions. Reference Link
📋 (Option 5 - Heatmap Table)
Monthly Breakdown
To provide even greater detail, we break down this performance further into monthly returns.
The color-coded table visually identifies periods of outperformance and underperformance for each company. Bold is used to highlight the single best-performing stock each month. This visual approach immediately clarifies temporal patterns, showcasing how monthly outcomes relate to volatility observations: Tesla’s exceptional returns in certain months correlate with its higher volatility profile.
| Monthly Stock Returns Over the Past 12-Month | ||||
| Color-coded by return level per company; bolded cells show the top-performing stock for each month | ||||
| Month | AAPL | META | TSLA | MSFT |
|---|---|---|---|---|
| 2024-04 | 2.71% | −10.70% | 29.02% | −2.90% |
| 2024-05 | 13.02% | 8.52% | −2.84% | 6.82% |
| 2024-06 | 9.56% | 8.12% | 11.12% | 7.67% |
| 2024-07 | 5.44% | −5.83% | 17.28% | −6.40% |
| 2024-08 | 3.24% | 9.79% | −7.74% | −0.11% |
| 2024-09 | 1.75% | 9.91% | 22.19% | 3.15% |
| 2024-10 | −3.04% | −0.85% | −4.50% | −5.57% |
| 2024-11 | 5.17% | 1.19% | 38.15% | 4.42% |
| 2024-12 | 5.52% | 2.03% | 17.00% | −0.46% |
| 2025-01 | −5.76% | 17.71% | 0.19% | −1.53% |
| 2025-02 | 2.59% | −3.04% | −27.59% | −4.16% |
| 2025-03 | −8.15% | −13.67% | −11.54% | −5.44% |
| 2025-04 | −13.04% | −15.91% | −12.22% | −4.33% |
| Cumulative Returns1 | 17.02% | 0.98% | 60.15% | −9.75% |
| Data source: Yahoo Finance, processed with tidyquant | ||||
| 1 Cumulative Returns = | ||||
| Monthly Stock Return Trends Over the Past 12-Month | ||||
| Each sparkline shows monthly return trajectory | ||||
| AAPL | META | TSLA | MSFT | |
|---|---|---|---|---|
| Monthly Trend | ||||
The sparklines reveals shifts in monthly return momentum for each ticker and their link to volatility.
Conclusion
Comprehensive View: Diverse visualizations collectively reveal a broad picture of recent tech stock performance.
Layered Analyses: Sequential methods from normalized price comparisons and rolling return-volatility studies to monthly breakdowns and innovative visual storytelling.
Future Directions: Expanding to broader market sectors or extending the timeframe can further generate insights.
